Egocentric Gaze Estimation via Neck-Mounted Camera

TL;DR

This paper explores gaze estimation from a neck-mounted camera, introducing a new dataset, evaluating a transformer-based model, and proposing extensions to improve accuracy in this novel viewpoint.

Contribution

It presents the first dataset for neck-mounted gaze estimation, evaluates a transformer-based model, and introduces auxiliary tasks and co-learning approaches.

Findings

Gaze out-of-bound classification improves performance.

Co-learning approach does not significantly enhance results.

Neck-mounted gaze estimation is feasible with specialized models.

Abstract

This paper introduces neck-mounted view gaze estimation, a new task that estimates user gaze from the neck-mounted camera perspective. Prior work on egocentric gaze estimation, which predicts device wearer's gaze location within the camera's field of view, mainly focuses on head-mounted cameras while alternative viewpoints remain underexplored. To bridge this gap, we collect the first dataset for this task, consisting of approximately 4 hours of video collected from 8 participants during everyday activities. We evaluate a transformer-based gaze estimation model, GLC, on the new dataset and propose two extensions: an auxiliary gaze out-of-bound classification task and a multi-view co-learning approach that jointly trains head-view and neck-view models using a geometry-aware auxiliary loss. Experimental results show that incorporating gaze out-of-bound classification improves performance over standard fine-tuning, while the co-learning approach does not yield gains. We further analyze these results and discuss implications for neck-mounted gaze estimation.